Previous work on assignment inference focused on visualizations of categorical information, where merit depends on direct associations [1]}, [2]}, [3]}. We propose that assignment inference also governs inferred mappings for visualizations of continuous data, where merit may depend on both direct and relational associations. As such, assignment inference would operate over multiple (sometimes competing) sources of merit to determine inferred mappings.
